class_docstr: Implementation of Dataset class
  
  This is a simple implementation of loading and processing dataset for GATNE model.
  
  Args:
      config: dict, some configure parameters.
class_docstr_tok: ["Implementation","of","Dataset","class","This","is","a","simple","implementation","of","loading","and","processing","dataset","for","GATNE","model",".","Args",":","config",":","dict",",","some","configure","parameters","."]
docstr: Load testing data from file. 
          
docstr_tok: ["Load","testing","data","from","file","."]
code: def load_test_data(self, file_):
      """Load testing data from file."""
      logging.info("loading data from %s" % file_)
  
      true_edge_data_by_type = {}
      fake_edge_data_by_type = {}
      with open(file_, "r") as reader:
          for line in reader:
              words = line.strip().split(" ")
              src, dst = (
                  self.word2index[words[1]],
                  self.word2index[words[2]],
              )
              e_type = words[0]
              if int(words[3]) == 1:  # true edges
                  if e_type not in true_edge_data_by_type:
                      true_edge_data_by_type[e_type] = list()
                  true_edge_data_by_type[e_type].append((src, dst))
              else:  # fake edges
                  if e_type not in fake_edge_data_by_type:
                      fake_edge_data_by_type[e_type] = list()
                  fake_edge_data_by_type[e_type].append((src, dst))
  
      return (true_edge_data_by_type, fake_edge_data_by_type)
  
file_path: PGL/legacy/examples/GATNE/Dataset.py
imports: import sys
  import os
  import tqdm
  import numpy
  import logging
  import random
  from pgl import heter_graph
  import pickle
label_desc: Use image node_id 4 for calling the Dataset obj's underlying member method code with example usage: obj.load_test_data(file_) and returns: true_edge_data_by_type,  fake_edge_data_by_type